I Know You Can't See Me: Dynamic Occlusion-Aware Safety Validation of
Strategic Planners for Autonomous Vehicles Using Hypergames
- URL: http://arxiv.org/abs/2109.09807v1
- Date: Mon, 20 Sep 2021 19:38:14 GMT
- Title: I Know You Can't See Me: Dynamic Occlusion-Aware Safety Validation of
Strategic Planners for Autonomous Vehicles Using Hypergames
- Authors: Maximilian Kahn, Atrisha Sarkar and Krzysztof Czarnecki
- Abstract summary: We develop a novel multi-agent dynamic occlusion risk measure for assessing situational risk.
We present a white-box, scenario-based, accelerated safety validation framework for assessing safety of strategic planners in AV.
- Score: 12.244501203346566
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: A particular challenge for both autonomous and human driving is dealing with
risk associated with dynamic occlusion, i.e., occlusion caused by other
vehicles in traffic. Based on the theory of hypergames, we develop a novel
multi-agent dynamic occlusion risk (DOR) measure for assessing situational risk
in dynamic occlusion scenarios. Furthermore, we present a white-box,
scenario-based, accelerated safety validation framework for assessing safety of
strategic planners in AV. Based on evaluation over a large naturalistic
database, our proposed validation method achieves a 4000% speedup compared to
direct validation on naturalistic data, a more diverse coverage, and ability to
generalize beyond the dataset and generate commonly observed dynamic occlusion
crashes in traffic in an automated manner.
Related papers
- LaPlaSS: Latent Space Planning for Stochastic Systems [8.529245639496274]
We propose a "generate-and-test" approach to risk-bounded planning for autonomous mobile agents.
We use a variational autoencoder to learn a latent linear dynamics model and encode the planning problem into the latent space to generate the candidate trajectory.
We demonstrate that our algorithm, LaPlaSS, is able to generate trajectory plans with bounded risk for a real-world agent with learned dynamics.
arXiv Detail & Related papers (2024-04-10T14:52:35Z) - SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Controllable Adversaries [94.84458417662407]
We introduce SAFE-SIM, a novel diffusion-based controllable closed-loop safety-critical simulation framework.
We develop a novel approach to simulate safety-critical scenarios through an adversarial term in the denoising process.
We validate our framework empirically using the NuScenes dataset, demonstrating improvements in both realism and controllability.
arXiv Detail & Related papers (2023-12-31T04:14:43Z) - Safety-aware Causal Representation for Trustworthy Offline Reinforcement
Learning in Autonomous Driving [33.672722472758636]
offline Reinforcement Learning(RL) approaches exhibit notable efficacy in addressing sequential decision-making problems from offline datasets.
We introduce the saFety-aware strUctured Scenario representatION ( Fusion) to facilitate the learning of a generalizable end-to-end driving policy.
Empirical evidence in various driving scenarios attests that Fusion significantly enhances the safety and generalizability of autonomous driving agents.
arXiv Detail & Related papers (2023-10-31T18:21:24Z) - CAT: Closed-loop Adversarial Training for Safe End-to-End Driving [54.60865656161679]
Adversarial Training (CAT) is a framework for safe end-to-end driving in autonomous vehicles.
Cat aims to continuously improve the safety of driving agents by training the agent on safety-critical scenarios.
Cat can effectively generate adversarial scenarios countering the agent being trained.
arXiv Detail & Related papers (2023-10-19T02:49:31Z) - A Counterfactual Safety Margin Perspective on the Scoring of Autonomous
Vehicles' Riskiness [52.27309191283943]
This paper presents a data-driven framework for assessing the risk of different AVs' behaviors.
We propose the notion of counterfactual safety margin, which represents the minimum deviation from nominal behavior that could cause a collision.
arXiv Detail & Related papers (2023-08-02T09:48:08Z) - RCP-RF: A Comprehensive Road-car-pedestrian Risk Management Framework
based on Driving Risk Potential Field [1.625213292350038]
We propose a comprehensive driving risk management framework named RCP-RF based on potential field theory under Connected and Automated Vehicles (CAV) environment.
Different from existing algorithms, the motion tendency between ego and obstacle cars and the pedestrian factor are legitimately considered in the proposed framework.
Empirical studies validate the superiority of our proposed framework against state-of-the-art methods on real-world dataset NGSIM and real AV platform.
arXiv Detail & Related papers (2023-05-04T01:54:37Z) - Unsupervised Self-Driving Attention Prediction via Uncertainty Mining
and Knowledge Embedding [51.8579160500354]
We propose an unsupervised way to predict self-driving attention by uncertainty modeling and driving knowledge integration.
Results show equivalent or even more impressive performance compared to fully-supervised state-of-the-art approaches.
arXiv Detail & Related papers (2023-03-17T00:28:33Z) - Generating and Characterizing Scenarios for Safety Testing of Autonomous
Vehicles [86.9067793493874]
We propose efficient mechanisms to characterize and generate testing scenarios using a state-of-the-art driving simulator.
We use our method to characterize real driving data from the Next Generation Simulation (NGSIM) project.
We rank the scenarios by defining metrics based on the complexity of avoiding accidents and provide insights into how the AV could have minimized the probability of incurring an accident.
arXiv Detail & Related papers (2021-03-12T17:00:23Z) - Automatic Clustering for Unsupervised Risk Diagnosis of Vehicle Driving
for Smart Road [20.544782390670512]
This study proposes a domain-specific automatic clustering (termed Autocluster) to self-learn the optimal models for unsupervised risk assessment.
Findings show that Autocluster is reliable and promising to diagnose multiple distinct risk exposures inherent to generalised driving behaviour.
arXiv Detail & Related papers (2020-11-24T07:15:03Z) - Risk-Sensitive Sequential Action Control with Multi-Modal Human
Trajectory Forecasting for Safe Crowd-Robot Interaction [55.569050872780224]
We present an online framework for safe crowd-robot interaction based on risk-sensitive optimal control, wherein the risk is modeled by the entropic risk measure.
Our modular approach decouples the crowd-robot interaction into learning-based prediction and model-based control.
A simulation study and a real-world experiment show that the proposed framework can accomplish safe and efficient navigation while avoiding collisions with more than 50 humans in the scene.
arXiv Detail & Related papers (2020-09-12T02:02:52Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.